1. Field of the Invention
The present invention relates generally to image compression systems, and more particularly to a system and method of discriminating among image characteristics in order to select among a plurality of compression techniques.
2. Description of Background Art
Modern image scanning equipment typically includes hardware and/or software technology for the compression of scanned images or documents. In recent years, with the proliferation of digitized images being transmitted along communications channels, particularly on the Internet, there has been a growing need for effective and efficient compression techniques. Many compression standards have emerged, with the relative effectiveness of each standard being dependent on the types of images being processed. Specifically, the most commonly-used compression standards and techniques are optimized for particular types of images; such methods tend to be less effective when applied to other image types. In addition, some techniques are "lossy", meaning that some image data is lost when the image is reconstructed, while others are "lossless", meaning that no image data is lost. For some images, lossy compression is acceptable, whereas for others it is not.
One relatively ubiquitous compression standard is the Joint Photographic Experts Group (JPEG) standard, described in W. B. Pennebaker & J. L. Mitchell, JPEG Still Image Data Compression Standard, Reinhold: 1993, pp. 389-94. JPEG is typically a lossy compression standard, though loss-less versions are available. JPEG operates by dividing each color component of the image into blocks of predefined dimension (such as eight pixels by eight pixels), performing discrete cosine transform (DCT) operations on square subregions in the image, truncating the precision of the terms which result, and finally performing a run-length compression. The JPEG standard is highly effective for grayscale and color images having multiple hues or shading. It is less effective, however, for bitonal images.
Other techniques yield good results when compressing bitonal images but may be less effective for grayscale. These include, for example, the 1-D Consultative Committee of the International Telephone and Telegraph (CCITT) Group 3 and Group 4 standards (G3 and G4), described in R. C. Gonzalez & R. E. Woods, Digital Image Processing, Addison-Wesley: 1992. G3 performs nonadaptive one-dimensional run-length coding with optional two-dimensional coding of the last K-1 lines of each group of K lines (for K=2 or 4). G4 is a simplified version of G3 in which only two-dimensional coding is performed.
Other standards are also known and available for bitonal image compression, such as for example the Joint Bilevel Imaging Group (JBIG) standard, ISO IS 11544, equivalent to ITU-T T.82, described in PM-2m User's Guide, Pixel Magic, October 1996, document #UG-10-10, revision 1.0. JBIG uses an adaptive algorithm, in contrast to the nonadaptive technique of G3 and G4.
One problem with existing compression schemes is that they tend to be optimized for a particular type of image, and are therefore less effective when processing some other type. For example, JPEG, while highly effective for grayscale images, is less effective for bitonal images and may result in a compressed image with little useful information. Conversely G3, G4, and JBIG, while useful for bitonal images, are less effective when applied to grayscale images and may result in a failure to compress effectively. In fact, in some cases a misapplied compression technique may fail to compress the image at all, and may even cause the image to take up more space than it did in its original form.
Accordingly, what is needed is a system and method for classifying an image according to some predetermined criterion or criteria, such as grayscale versus bitonal, in order to select a compression technique that is well-adapted to the image being processed.
Many image scanning apparatuses pass a physical representation of the image, such as a sheet of paper, over an optical scanning mechanism. Pre-scanning of an image in order to perform classification may result in slower processing time due to the fact that the image must be scanned twice (once for classification, and a second time for compression). In high-volume operations, where this may result in doubling of processing time, such a reduction in efficiency may be unacceptable.
Thus, what is further needed is a system and method that is capable of performing the above-mentioned classification without introducing additional scanning time in the processing of images.